Last updated on October 1st, 2026 at 11:17 am
I will tell you the truth: when I first ran LLMs locally, I thought I was doing what people with servers in their basements did. Turns out, I was wrong.
I tested various configurations over the last weekend, and my local LLM started working on my mid-range PC. Here is how to do it, what didn’t work, and what did.
Table of Contents
Why I Even Bothered with Local LLMs
Before we build, I want to explain why I went down this rabbit hole. I got fed up waiting 2-3 seconds for ChatGPT to respond, and I am not particularly fond of sending my data to third-party servers. Local LLMs had sub-10ms response times and total privacy. That alone got me curious.
And even at more than 10,000 API calls each month, you’ll save money in just a year by going local. That is not a scale everyone needs, but it is good to know.
Step 1: Evaluate whether your hardware can do it.
Straight talk: you don’t actually need an $8,000 graphics card. My card is an RTX 3060, 12GB VRAM, 32GB system RAM, and it is not problematic with 7B-13B models.
The sweet spot for beginners:
CPU: 2.5GHz (Nvidia 3060 / 3080 or higher)
- GPU: at least 12GB minimum VRAM) (RTX 3060 / 3080 or greater )
- RAM: 16-32GB system memory
- Storage: accelerated SSD (prices are 4-8GB each)
The 24GB VRAM will support 70B quantization models. But honestly? Start small. The 7B model will surprise you with what it can do.
Step 2: Selecting the appropriate tool (I Tried Three of them)
I tested Ollama (on GitHub), LM Studio, and GPT4All. Here’s my take:
Ollama won for me. It is command-line, and that does not sound easy, but it is, in fact, easier. You run ollama run llama3, and bang, you are already communicating with an AI—no configuration hell.
LM Studio does not have terminals. It has a shiny GUI, lets you window-shop models, and does it all with clicks. I would suggest this when you are not computer savvy.
GPT4All was best regarded as the easiest to use, but I found it stiffer when I wanted easy access.
For this guide, I am using Ollama, since it is what I have stuck with.
Step 3: Ollama Installation (5-minute installation)
I use Windows, so I downloaded the installer from ollama.com. Mac and Linux users can install it even more easily with their package managers.
The installation was followed by the following command that I typed in my terminal:
ollama run llama3
That’s it. Ollama automatically downloaded the Llama 3 model (around 4.7GB) and booted it. It took a little longer (around 10 seconds) to respond, but that was because it was loading. After that? Lightning fast.
Step 4: Testing the Dissimilar Models.
Here’s where it got fun. The library of models that you can use in Ollama is:
- Llama 3 (8B): Versatile; has few problems with tasks.
- Qwen 2.5 (7B): Quite good at coding questions.
- Mistral (7B): Reasoned, thought themself superior during discussions.
I kept switching between them by running ollama run [model-name]. Each model has different advantages, and I had some installed.
Step 5: To make It Realistically Useful with RAG.
Now running a base model is fun, but here is what made it viable to me: I linked it to my personal documents with what is termed as Running a base model is cool, but that is not the end, what made it possible in my case was connecting it to my personal documents that is known as RAG (Retrieval-Augmented Generation).
Imagine it this way: the AI doesn’t guess answers; it searches papers and answers based on the information it finds. I used open-webui/open-webui, a web interface for uploading documents that connects automatically.
Setup took about 20 minutes. I can now ask questions about my project notes, and it knows what I am talking about.
Step 6: Optimizing for Speed
My 7B model came out of the box at approximately 25 tokens per second. Not bad, but I wanted faster. Here’s what helped:
Quantization: I converted myself to 4-bit quantized models. They are compressed versions that consume much less memory. My RTX 3060 no longer struggled with 13B models and ran them smoothly.
Below is an example of how to use a quantized model in Ollama: ollama run llama3:7b-q40
The q40 in that implies it is 4-bit quantized. Speed jumped to 40+ tokens/second, and honestly, I couldn’t tell the difference in quality.
What Actually Surprised Me
Three things I didn’t expect:
- It is much less technical than I imagined. If you can install software and copy-paste commands, then that is good.
- Offline mode is amazing. I tried it on one of the flights; there was no internet, and even my local AI did not fail. Can’t do that with ChatGPT.
- The community is huge. When I needed help, I found solutions in the LocalLLaMA section of Reddit within a few minutes. Human beings are really co-operative.
Should You Build One?
If you use ChatGPT a couple of times a week, it’s probably not worth it. But if you:
- Care about privacy
- Need quick responses you can code or write.
- Process sensitive data
- And you don’t want to fuss with technology.
Then all right, get a weekend off on this. My initial state of complete ignorance became a working setup in 6-7 hours of practical work (the remaining one was waiting for downloads).
It begins with Ollama and a 7B model. Upgrading your hardware is always an option, and you can always move to bigger models later if it clicks. The barrier to entry in 2025 will be lower than you might think, and, frankly, having a local AI running is satisfying.
Read:
Also Read: ChatGPT vs Gemini: Which AI Assistant Wins for Students and IT Pros in 2025?
I’m a technology writer passionate about AI and digital marketing. I create engaging and useful content that bridges the gap between complex technology concepts and digital technologies. My writing makes the process easy and engaging. I encourage participation I continue to research innovation and technology. Let’s connect and talk technology!
